标题： 基于核条件依赖度量的马尔可夫毯排序 显示英文标题

标题： Markov Blanket Ranking using Kernel-based Conditional Dependence Measures

摘要： 开发超越纯相关分析而进行更多因果分析的特征选择算法是科学领域的一个重要问题。 一些算法试图通过发现目标的马尔可夫毯来实现这一点，但它们都包含一个前向选择步骤，变量必须通过这个步骤才能被纳入条件集。 因此，这些算法可能不会考虑所有可能的条件多变量组合。 我们通过提出一种使用基于核的条件依赖度量的后向消除方法来改进这一局限性，该方法以完全多变量的方式识别马尔可夫毯。 该算法易于实现，并且在合成数据集和真实数据集上的表现优于其他方法。 显示英文摘要

摘要： Developing feature selection algorithms that move beyond a pure correlational to a more causal analysis of observational data is an important problem in the sciences. Several algorithms attempt to do so by discovering the Markov blanket of a target, but they all contain a forward selection step which variables must pass in order to be included in the conditioning set. As a result, these algorithms may not consider all possible conditional multivariate combinations. We improve on this limitation by proposing a backward elimination method that uses a kernel-based conditional dependence measure to identify the Markov blanket in a fully multivariate fashion. The algorithm is easy to implement and compares favorably to other methods on synthetic and real datasets.